Board Diversity and Earnings Management in Nigeria Empirical Evidence from Financial Distress Firms

Christina Nwachukwu
nwachukwuchristina@gmail.com
John U. Ihendinihu
ihendinihu.john@gmail.com
Department of Accounting,
College of Management Sciences,
Michael Okpara University of Agriculture,
Umudike, Abia State

Past empirical evidence generally suggests that the presence of female directors on corporate
boards tends to improve earnings quality due to their superior monitoring abilities. Unlike
previous studies that focus on accrual-based earnings management and real earnings
management, this study analyses small positive net profit (earnings management proxy) and
investigates the probability that the inclusion of more female directors and independent directors
among financially distressed firms will help reduce managers’ net profit manipulation. Using a
sample of 179 firm-year observations obtained by implementing Altman’s Z_ score model for listed
non-financial firms in Nigeria for the period 2011 to 2020, binary logistic regression analysis
reveals that the inclusion of more female director to participate in board activities is associated
with lower levels of net profit manipulation likelihood. This finding aligns well with resource
dependence and social theories which supports female directors’ monitoring efficacy and note that
female directors are more conservative and risk-averse than their male counterpart. Most
interestingly, the study outcome suggest that higher board independence fail to curb earnings
management for financially distressed companies in Nigeria. However, the study recommends
among others that female participation on corporate boards should be given kin priority since
such policies when implemented have been empirically proven to serve as a good corporate
governance tool effective in curbing earnings management activities of managers which will
ultimately provide stakeholders with higher quality earnings reports.
Keywords: Board Gender Diversity, Earnings Management, Financially Distressed Firms, Binary
Logistic Regression

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